Method and apparatus for adaptive point cloud attribute coding

ABSTRACT

A method of adaptive point cloud attribute coding is performed by at least one processor and includes determining a centroid of k candidate points of a point cloud, k being a predetermined sampling rate of all points of the point cloud, and selecting one of the k candidate points that is closest to the centroid. The method further includes determining a first levels-of-detail (LoD) layer comprising the one of the k candidate points, and a second LoD layer without the one of the k candidate points, and performing coding of attributes of the all points, based on the first LoD layer and the second LoD layer.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from U.S. Provisional PatentApplication No. 62/870,663, filed on Jul. 3, 2019, in the U.S. Patentand Trademark Office, which is incorporated herein by reference in itsentirety.

BACKGROUND 1. Field

Methods and apparatuses consistent with embodiments relate tograph-based point cloud compression (G-PCC), and more particularly, amethod and an apparatus for adaptive point cloud attribute coding.

2. Description of Related Art

Advanced three-dimensional (3D) representations of the world areenabling more immersive forms of interaction and communication, and alsoallow machines to understand, interpret and navigate our world. 3D pointclouds have emerged as an enabling representation of such information. Anumber of use cases associated with point cloud data have beenidentified, and corresponding requirements for point cloudrepresentation and compression have been developed.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g., color, material properties, etc. Point clouds can beused to reconstruct an object or a scene as a composition of suchpoints. They can be captured using multiple cameras and depth sensors invarious setups, and may be made up of thousands up to billions of pointsto realistically represent reconstructed scenes.

Compression technologies are needed to reduce the amount of data torepresent a point cloud. As such, technologies are needed for lossycompression of point clouds for use in real-time communications and sixdegrees of freedom (6DoF) virtual reality. In addition, technology issought for lossless point cloud compression in the context of dynamicmapping for autonomous driving and cultural heritage applications, etc.The Moving Picture Experts Group (MPEG) has started working on astandard to address compression of geometry and attributes such ascolors and reflectance, scalable/progressive coding, coding of sequencesof point clouds captured over time, and random access to subsets of apoint cloud.

FIG. 1 is a diagram illustrating a method of generating levels of detail(LoD) in G-PCC.

Referring to FIG. 1, in current G-PCC attributes coding, an LoD (i.e., agroup) of each 3D point (e.g., P0-P9) is generated based on a distanceof each 3D point, and then attribute values of 3D points in each LoD isencoded by applying prediction in an LoD-based order 110 instead of anoriginal order 105 of the 3D points. For example, an attributes value ofthe 3D point P2 is predicted by calculating a distance-based weightedaverage value of the 3D points P0, P5 and P4 that were encoded ordecoded prior to the 3D point P2.

In current G-PCC attributes coding, an original point cloud may beregrouped into multiple LoD layers, based upon several methods. Thensubsequent steps such as prediction and lifting are performed based uponthis reorganized point structure.

For LoD generation, a G-PCC codec may utilize two different sub-samplingmethods for building LoD layers for a lifting scheme: distance-basedsampling and fixed-rate regular sampling.

In fixed-rate regular sampling, let I_(L-1) be a set of Morton-orderedindices and LOD_(L-1) be lowest layer LoD points that represent anentire point cloud. Here, L represents a total number of LoD layers. Thefixed-rate regular sampling method includes selecting every k^(th) indexfrom I_(L-1) starting at a first index. The indices that are notselected represent points in LOD_(L-2). The indices that are selectedare subjected to the same process L−1 more times by which upper levelLoD layers are generated. G-PCC has a set sampling rate k=4.

The fixed-rate regular sampling method described above generates LoDlayers that capture a global point distribution over anirregularly-sampled sparse point cloud. However, this method completelyignores a local point distribution as indices are selected blindly inevery interval of k points, neglecting a distribution of (k−1)^(th)points before k^(th) points.

SUMMARY

According to embodiments, a method of adaptive point cloud attributecoding is performed by at least one processor and includes determining acentroid of k candidate points of a point cloud, k being a predeterminedsampling rate of all points of the point cloud, and selecting one of thek candidate points that is closest to the centroid. The method furtherincludes determining a first levels-of-detail (LoD) layer comprising theone of the k candidate points, and a second LoD layer without the one ofthe k candidate points, and performing coding of attributes of the allpoints, based on the first LoD layer and the second LoD layer.

According to embodiments, an apparatus for adaptive point cloudattribute coding includes at least one memory configured to storecomputer program code, and at least one processor configured to accessthe at least one memory and operate according to the computer programcode. The computer program code includes first determining codeconfigured to cause the at least one processor to determine a centroidof k candidate points of a point cloud, k being a predetermined samplingrate of all points of the point cloud, and selecting code configured tocause the at least one processor to select one of the k candidate pointsthat is closest to the centroid. The computer program code furtherincludes second determining code configured to cause the at least oneprocessor to determine a first levels-of-detail (LoD) layer comprisingthe one of the k candidate points, and a second LoD layer without theone of the k candidate points, and performing code configured to causethe at least one processor to perform coding of attributes of the allpoints, based on the first LoD layer and the second LoD layer.

A non-transitory computer-readable storage medium storing instructionsthat cause at least one processor to determine a centroid of k candidatepoints of a point cloud, k being a predetermined sampling rate of allpoints of the point cloud, and select one of the k candidate points thatis closest to the centroid. The instructions further cause the at leastone processor to determine a first levels-of-detail (LoD) layercomprising the one of the k candidate points, and a second LoD layerwithout the one of the k candidate points, and perform coding ofattributes of the all points, based on the first LoD layer and thesecond LoD layer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a method of generating LoD in G-PCC.

FIG. 2 is a block diagram of a communication system according toembodiments.

FIG. 3 is a diagram of a placement of a G-PCC compressor and a G-PCCdecompressor in an environment, according to embodiments.

FIG. 4 is a functional block diagram of the G-PCC compressor accordingto embodiments.

FIG. 5 is a functional block diagram of the G-PCC decompressor accordingto embodiments.

FIG. 6 is a flowchart illustrating a method of adaptive point cloudattribute coding, according to embodiments.

FIG. 7 is a block diagram of an apparatus for adaptive point cloudattribute coding, according to embodiments.

FIG. 8 is a diagram of a computer system suitable for implementingembodiments.

DETAILED DESCRIPTION

Embodiments described herein provide a method and an apparatus foradaptive point cloud attribute coding. The method and apparatus take alocal point distribution into account and select an index out of kindices based on local point statistics.

FIG. 2 is a block diagram of a communication system 200 according toembodiments. The communication system 200 may include at least twoterminals 210 and 220 interconnected via a network 250. Forunidirectional transmission of data, a first terminal 210 may code pointcloud data at a local location for transmission to a second terminal 220via the network 250. The second terminal 220 may receive the coded pointcloud data of the first terminal 210 from the network 250, decode thecoded point cloud data and display the decoded point cloud data.Unidirectional data transmission may be common in media servingapplications and the like.

FIG. 2 further illustrates a second pair of terminals 230 and 240provided to support bidirectional transmission of coded point cloud datathat may occur, for example, during videoconferencing. For bidirectionaltransmission of data, each terminal 230 or 240 may code point cloud datacaptured at a local location for transmission to the other terminal viathe network 250. Each terminal 230 or 240 also may receive the codedpoint cloud data transmitted by the other terminal, may decode the codedpoint cloud data and may display the decoded point cloud data at a localdisplay device.

In FIG. 2, the terminals 210-240 may be illustrated as servers, personalcomputers and smartphones, but principles of the embodiments are not solimited. The embodiments find application with laptop computers, tabletcomputers, media players and/or dedicated video conferencing equipment.The network 250 represents any number of networks that convey codedpoint cloud data among the terminals 210-240, including for examplewireline and/or wireless communication networks. The communicationnetwork 250 may exchange data in circuit-switched and/or packet-switchedchannels. Representative networks include telecommunications networks,local area networks, wide area networks and/or the Internet. For thepurposes of the present discussion, an architecture and topology of thenetwork 250 may be immaterial to an operation of the embodiments unlessexplained herein below.

FIG. 3 is a diagram of a placement of a G-PCC compressor 303 and a G-PCCdecompressor 310 in an environment, according to embodiments. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, including, for example, video conferencing,digital TV, storing of compressed point cloud data on digital mediaincluding CD, DVD, memory stick and the like, and so on.

A streaming system 300 may include a capture subsystem 313 that caninclude a point cloud source 301, for example a digital camera,creating, for example, uncompressed point cloud data 302. The pointcloud data 302 having a higher data volume can be processed by the G-PCCcompressor 303 coupled to the point cloud source 301. The G-PCCcompressor 303 can include hardware, software, or a combination thereofto enable or implement aspects of the disclosed subject matter asdescribed in more detail below. Encoded point cloud data 304 having alower data volume can be stored on a streaming server 305 for futureuse. One or more streaming clients 306 and 308 can access the streamingserver 305 to retrieve copies 307 and 309 of the encoded point clouddata 304. A client 306 can include the G-PCC decompressor 310, whichdecodes an incoming copy 307 of the encoded point cloud data and createsoutgoing point cloud data 311 that can be rendered on a display 312 orother rendering devices (not depicted). In some streaming systems, theencoded point cloud data 304, 307 and 309 can be encoded according tovideo coding/compression standards. Examples of those standards includethose being developed by MPEG for G-PCC.

FIG. 4 is a functional block diagram of a G-PCC compressor 303 accordingto embodiments.

As shown in FIG. 4, the G-PCC compressor 303 includes a quantizer 405, apoints removal module 410, an octree encoder 415, an attributes transfermodule 420, an LoD generator 425, a prediction module 430, a quantizer435 and an arithmetic coder 440.

The quantizer 405 receives positions of points in an input point cloud.The positions may be (x,y,z)-coordinates. The quantizer 405 furtherquantizes the received positions, using, e.g., a scaling algorithmand/or a shifting algorithm.

The points removal module 410 receives the quantized positions from thequantizer 405, and removes or filters duplicate positions from thereceived quantized positions.

The octree encoder 415 receives the filtered positions from the pointsremoval module 410, and encodes the received filtered positions intooccupancy symbols of an octree representing the input point cloud, usingan octree encoding algorithm. A bounding box of the input point cloudcorresponding to the octree may be any 3D shape, e.g., a cube.

The octree encoder 415 further reorders the received filtered positions,based on the encoding of the filtered positions.

The attributes transfer module 420 receives attributes of points in theinput point cloud. The attributes may include, e.g., a color or RGBvalue and/or a reflectance of each point. The attributes transfer module420 further receives the reordered positions from the octree encoder415.

The attributes transfer module 420 further updates the receivedattributes, based on the received reordered positions. For example, theattributes transfer module 420 may perform one or more amongpre-processing algorithms on the received attributes, the pre-processingalgorithms including, for example, weighting and averaging the receivedattributes and interpolation of additional attributes from the receivedattributes. The attributes transfer module 420 further transfers theupdated attributes to the prediction module 430.

The LoD generator 425 receives the reordered positions from the octreeencoder 415, and obtains an LoD of each of the points corresponding tothe received reordered positions. Each LoD may be considered to be agroup of the points, and may be obtained based on a distance of each ofthe points. For example, as shown in FIG. 1, points P0, P5, P4 and P2may be in an LoD LOD0, points P0, P5, P4, P2, P1, P6 and P3 may be in anLoD LOD1, and points P0, P5, P4, P2, P1, P6, P3, P9, P8 and P7 may be inan LoD LOD2.

The prediction module 430 receives the transferred attributes from theattributes transfer module 420, and receives the obtained LoD of each ofthe points from the LoD generator 425. The prediction module 430 obtainsprediction residuals (values) respectively of the received attributes byapplying a prediction algorithm to the received attributes in an orderbased on the received LoD of each of the points. The predictionalgorithm may include any among various prediction algorithms such as,e.g., interpolation, weighted average calculation, a nearest neighboralgorithm and rate distortion optimization (RDO).

For example, as shown in FIG. 1, the prediction residuals respectivelyof the received attributes of the points P0, P5, P4 and P2 included inthe LoD LOD0 may be obtained first prior to those of the receivedattributes of the points P1, P6, P3, P9, P8 and P7 included respectivelyin the LoD layers LOD1 and LOD2. The prediction residuals of thereceived attributes of the point P2 may be obtained by calculating adistance based on a weighted average of the points P0, P5 and P4.

The quantizer 435 receives the obtained prediction residuals from theprediction module 430, and quantizes the received predicted residuals,using, e.g., a scaling algorithm and/or a shifting algorithm.

The arithmetic coder 440 receives the occupancy symbols from the octreeencoder 415, and receives the quantized prediction residuals from thequantizer 435. The arithmetic coder 440 performs arithmetic coding onthe received occupancy symbols and quantized predictions residuals toobtain a compressed bitstream. The arithmetic coding may include anyamong various entropy encoding algorithms such as, e.g.,context-adaptive binary arithmetic coding.

FIG. 5 is a functional block diagram of a G-PCC decompressor 310according to embodiments.

As shown in FIG. 5, the G-PCC decompressor 310 includes an arithmeticdecoder 505, an octree decoder 510, an inverse quantizer 515, an LoDgenerator 520, an inverse quantizer 525 and an inverse prediction module530.

The arithmetic decoder 505 receives the compressed bitstream from theG-PCC compressor 303, and performs arithmetic decoding on the receivedcompressed bitstream to obtain the occupancy symbols and the quantizedprediction residuals. The arithmetic decoding may include any amongvarious entropy decoding algorithms such as, e.g., context-adaptivebinary arithmetic decoding.

The octree decoder 510 receives the obtained occupancy symbols from thearithmetic decoder 505, and decodes the received occupancy symbols intothe quantized positions, using an octree decoding algorithm.

The inverse quantizer 515 receives the quantized positions from theoctree decoder 510, and inverse quantizes the received quantizedpositions, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed positions of the points in the input pointcloud.

The LoD generator 520 receives the quantized positions from the octreedecoder 510, and obtains the LoD of each of the points corresponding tothe received quantized positions.

The inverse quantizer 525 receives the obtained quantized predictionresiduals, and inverse quantizes the received quantized predictionresiduals, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed prediction residuals.

The inverse prediction module 530 receives the obtained reconstructedprediction residuals from the inverse quantizer 525, and receives theobtained LoD of each of the points from the LoD generator 520. Theinverse prediction module 530 obtains reconstructed attributesrespectively of the received reconstructed prediction residuals byapplying a prediction algorithm to the received reconstructed predictionresiduals in an order based on the received LoD of each of the points.The prediction algorithm may include any among various predictionalgorithms such as, e.g., interpolation, weighted average calculation, anearest neighbor algorithm and RDO. The reconstructed attributes are ofthe points in the input point cloud.

The method and the apparatus for adaptive point cloud attribute codingwill now be described in detail. Such a method and an apparatus may beimplemented in the G-PCC compressor 303 described above, namely, theprediction module 430. The method and the apparatus may also beimplemented in the G-PCC decompressor 310, namely, the inverseprediction module 530.

Mean-Based Local Point Distribution Capture

In embodiments, a centroid of all k candidate points is computed, andthen a point closest to the centroid, among the k candidate points, ischosen to be included in a lower level LoD layer, e.g., LOD2 shown inFIG. 1. This process is repeated until a number of points included thelower level LoD layer reaches a maximum value. Unselected points areincluded in an upper level LoD layer, e.g., LOD1 shown in FIG. 1.

In these embodiments, every point is taken into consideration togenerate more optimal LoD layers. A sampling rate, however, is chosenspecific to a point cloud.

In an example, one can set k=3 for sparse point clouds and k=4 for densepoint clouds.

Because G-PCC has a fixed LoD layer count L based on a sampling ratek=4, if a different value of the sampling rate is chosen, the LoD layercount L needs to be recalculated. The new LoD layer count L may becalculated using Equation (1):

$\begin{matrix}{{L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},} & (1)\end{matrix}$

where N is a number of points in a point cloud, and k is the samplingrate.

Median-Based Local Point Distribution Capture

In embodiments, next k candidate local points are collected, and amedian of the k candidate local points (a distribution) is found. Thisis done by sorting points one axis at a time. Starting with an x-axis,points are sorted based on x-coordinate values, and a median of thex-coordinate values is found. The same process is repeated for a y-axisand a z-axis, and a median for each of the y-axis and the z-axis iscomputed.

Then, a point closest to the median is selected as a subsampled pointamong the k candidate local points, to be included in a lower level LoDlayer, e.g., LOD2 shown in FIG. 1. This process is repeated until anumber of points included the lower level LoD layer reaches a maximumvalue. Unselected points are included in an upper level LoD layer, e.g.,LOD1 shown in FIG. 1.

A sampling rate k can be varied as discussed above with respect to themean-based method, and an LoD layer count L is recomputed using Equation(1) if the sampling rate k is varied, i.e., chosen to be a differentvalue.

Referring to FIGS. 4 and 5, each of the LoD generator 425 and the LoDgenerator 520 may perform the above-described mean-based local pointdistribution capture and median-based local point distribution capture.

FIG. 6 is a flowchart illustrating a method 600 of adaptive point cloudattribute coding, according to embodiments. In some implementations, oneor more process blocks of FIG. 6 may be performed by the G-PCCdecompressor 310. In some implementations, one or more process blocks ofFIG. 6 may be performed by another device or a group of devices separatefrom or including the G-PCC decompressor 310, such as the G-PCCcompressor 303.

Referring to FIG. 6, in a first block 610, the method 600 includesdetermining a centroid of k candidate points of a point cloud, k being apredetermined sampling rate of all points of the point cloud.

In a second block 620, the method 600 includes selecting one of the kcandidate points that is closest to the centroid.

In a third block 630, the method 600 includes determining a firstlevels-of-detail (LoD) layer comprising the one of the k candidatepoints, and a second LoD layer without the one of the k candidatepoints.

In a fourth block 640, the method 600 includes performing coding ofattributes of the all points, based on the first LoD layer and thesecond LoD layer.

Based on the point cloud being a sparse point cloud, the predeterminedsampling rate may be 3.

Based on the point cloud being a dense point cloud, the predeterminedsampling rate may be 4.

Based on the predetermined sampling rate not being 4, a count of aplurality of LoD layers to be used to perform the coding of theattributes may be calculated based on Equation (1).

The method 600 may further include calculating a median of the kcandidate points, and selecting another one of the k candidate pointsthat is closest to the median. The determining the first LoD layer andthe second LoD layer may include determining the first LoD layercomprising the other one of the k candidate points, and the second LoDlayer without the other one of the k candidate points.

The calculating the median may include sorting x-coordinate values ofthe k candidate points, y-coordinate values of the k candidate points,and z-coordinate values of the k candidate points, and calculating amedian of each of the sorted x-coordinate values, the sortedy-coordinate values and the sorted z-coordinate values, as the median ofthe k candidate points.

Although FIG. 6 shows example blocks of the method 600, in someimplementations, the method 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of the method 600 may be performed in parallel.

Further, the proposed methods may be implemented by processing circuitry(e.g., one or more processors or one or more integrated circuits). In anexample, the one or more processors execute a program that is stored ina non-transitory computer-readable medium to perform one or more of theproposed methods.

FIG. 7 is a block diagram of an apparatus 700 for adaptive point cloudattribute coding, according to embodiments.

Referring to FIG. 7, the apparatus 700 includes first determining code710, selecting code 720, second determining code 730 and performing code740.

The first determining code 710 is configured to cause at least oneprocessor to determine a centroid of k candidate points of a pointcloud, k being a predetermined sampling rate of all points of the pointcloud.

The selecting code 720 is configured to cause the at least one processorto select one of the k candidate points that is closest to the centroid.

The second determining code 730 is configured to cause the at least oneprocessor to determine a first levels-of-detail (LoD) layer comprisingthe one of the k candidate points, and a second LoD layer without theone of the k candidate points.

The performing code 740 is configured to cause the at least oneprocessor to perform coding of attributes of the all points, based onthe first LoD layer and the second LoD layer.

Based on the point cloud being a sparse point cloud, the predeterminedsampling rate may be 3.

Based on the point cloud being a dense point cloud, the predeterminedsampling rate may be 4.

Based on the predetermined sampling rate not being 4, a count of aplurality of LoD layers to be used to perform the coding of theattributes may be calculated based on Equation (1).

The first determining code 710 may be further configured to cause the atleast one processor to calculate a median of the k candidate points. Theselecting code 720 may be further configured to cause the at least oneprocessor to select another one of the k candidate points that isclosest to the median. The second determining code 730 may be furtherconfigured to cause the at least one processor to determine the firstLoD layer comprising the other one of the k candidate points, and thesecond LoD layer without the other one of the k candidate points.

The first determining code 710 may be further configured to cause the atleast one processor to sort x-coordinate values of the k candidatepoints, y-coordinate values of the k candidate points, and z-coordinatevalues of the k candidate points, and calculate a median of each of thesorted x-coordinate values, the sorted y-coordinate values and thesorted z-coordinate values, as the median of the k candidate points.

FIG. 8 is a diagram of a computer system 800 suitable for implementingembodiments.

Computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code including instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by computer central processing units (CPUs),Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 8 for the computer system 800 are examplesin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementing theembodiments. Neither should the configuration of the components beinterpreted as having any dependency or requirement relating to any oneor combination of the components illustrated in the embodiments of thecomputer system 800.

The computer system 800 may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): a keyboard 801, a mouse 802, a trackpad 803, atouchscreen 810, a joystick 805, a microphone 806, a scanner 807, and acamera 808.

The computer system 800 may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouchscreen 810 or the joystick 805, but there can also be tactilefeedback devices that do not serve as input devices), audio outputdevices (such as: speakers 809, headphones (not depicted)), visualoutput devices (such as screens 810 to include cathode ray tube (CRT)screens, liquid-crystal display (LCD) screens, plasma screens, organiclight-emitting diode (OLED) screens, each with or without touchscreeninput capability, each with or without tactile feedback capability—someof which may be capable to output two dimensional visual output or morethan three dimensional output through means such as stereographicoutput; virtual-reality glasses (not depicted), holographic displays andsmoke tanks (not depicted)), and printers (not depicted). A graphicsadapter 850 generates and outputs images to the touchscreen 810.

The computer system 800 can also include human accessible storagedevices and their associated media such as optical media including aCD/DVD ROM/RW drive 820 with CD/DVD or the like media 821, a thumb drive822, a removable hard drive or solid state drive 823, legacy magneticmedia such as tape and floppy disc (not depicted), specializedROM/ASIC/PLD based devices such as security dongles (not depicted), andthe like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

The computer system 800 can also include interface(s) to one or morecommunication networks 855. The communication networks 855 can forexample be wireless, wireline, optical. The networks 855 can further belocal, wide-area, metropolitan, vehicular and industrial, real-time,delay-tolerant, and so on. Examples of the networks 855 include localarea networks such as Ethernet, wireless LANs, cellular networks toinclude global systems for mobile communications (GSM), third generation(3G), fourth generation (4G), fifth generation (5G), Long-Term Evolution(LTE), and the like, TV wireline or wireless wide area digital networksto include cable TV, satellite TV, and terrestrial broadcast TV,vehicular and industrial to include CANBus, and so forth. The networks855 commonly require external network interface adapters that attachedto certain general purpose data ports or peripheral buses 849 (such as,for example universal serial bus (USB) ports of the computer system 800;others are commonly integrated into the core of the computer system 800by attachment to a system bus as described below, for example, a networkinterface 854 including an Ethernet interface into a PC computer systemand/or a cellular network interface into a smartphone computer system.Using any of these networks 855, the computer system 800 can communicatewith other entities. Such communication can be uni-directional, receiveonly (for example, broadcast TV), uni-directional send-only (for exampleCANbus to certain CANbus devices), or bi-directional, for example toother computer systems using local or wide area digital networks.Certain protocols and protocol stacks can be used on each of thosenetworks 855 and network interfaces 854 as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces 854 can be attached to a core 840 of thecomputer system 800.

The core 840 can include one or more Central Processing Units (CPU) 841,Graphics Processing Units (GPU) 842, specialized programmable processingunits in the form of Field Programmable Gate Areas (FPGA) 843, hardwareaccelerators 844 for certain tasks, and so forth. These devices, alongwith read-only memory (ROM) 845, random-access memory (RAM) 846,internal mass storage 847 such as internal non-user accessible harddrives, solid-state drives (SSDs), and the like, may be connectedthrough a system bus 848. In some computer systems, the system bus 848can be accessible in the form of one or more physical plugs to enableextensions by additional CPUs, GPU, and the like. The peripheral devicescan be attached either directly to the core's system bus 848, or throughthe peripheral buses 849. Architectures for a peripheral bus includeperipheral component interconnect (PCI), USB, and the like.

The CPUs 841, GPUs 842, FPGAs 843, and hardware accelerators 844 canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in theROM 845 or RAM 846. Transitional data can also be stored in the RAM 846,whereas permanent data can be stored for example, in the internal massstorage 847. Fast storage and retrieve to any of the memory devices canbe enabled through the use of cache memory, that can be closelyassociated with the CPU 841, GPU 842, internal mass storage 847, ROM845, RAM 846, and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of embodiments, or they can be of the kind well known andavailable to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system 800having architecture, and specifically the core 840 can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core 840 that are of non-transitorynature, such as the core-internal mass storage 847 or ROM 845. Thesoftware implementing various embodiments can be stored in such devicesand executed by the core 840. A computer-readable medium can include oneor more memory devices or chips, according to particular needs. Thesoftware can cause the core 840 and specifically the processors therein(including CPU, GPU, FPGA, and the like) to execute particular processesor particular parts of particular processes described herein, includingdefining data structures stored in the RAM 846 and modifying such datastructures according to the processes defined by the software. Inaddition or as an alternative, the computer system can providefunctionality as a result of logic hardwired or otherwise embodied in acircuit (for example: the hardware accelerator 844), which can operatein place of or together with software to execute particular processes orparticular parts of particular processes described herein. Reference tosoftware can encompass logic, and vice versa, where appropriate.Reference to a computer-readable media can encompass a circuit (such asan integrated circuit (IC)) storing software for execution, a circuitembodying logic for execution, or both, where appropriate. Embodimentsencompass any suitable combination of hardware and software.

While this disclosure has described several embodiments, there arealterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods that, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

The invention claimed is:
 1. A method of adaptive point cloud attributecoding, the method being performed by at least one processor, and themethod comprising: determining a centroid of k candidate points of apoint cloud, k being a predetermined sampling rate of all points of thepoint cloud; selecting one of the k candidate points that is closest tothe centroid; determining a first levels-of-detail (LoD) layercomprising the one of the k candidate points, and a second LoD layerwithout the one of the k candidate points; and performing coding ofattributes of the all points, based on the first LoD layer and thesecond LoD layer.
 2. The method of claim 1, wherein, based on the pointcloud being a sparse point cloud, the predetermined sampling rate is 3.3. The method of claim 1, wherein, based on the point cloud being adense point cloud, the predetermined sampling rate is
 4. 4. The methodof claim 1, wherein, based on the predetermined sampling rate not being4, a count of a plurality of LoD layers to be used to perform the codingof the attributes is calculated based on an equation as follows:${L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},$ where Nis a number of the all points, and k is the predetermined sampling rate.5. The method of claim 1, further comprising: calculating a median ofthe k candidate points; and selecting another one of the k candidatepoints that is closest to the median, wherein the determining the firstLoD layer and the second LoD layer comprises determining the first LoDlayer comprising the other one of the k candidate points, and the secondLoD layer without the other one of the k candidate points.
 6. The methodof claim 5, wherein the calculating the median comprises: sortingx-coordinate values of the k candidate points, y-coordinate values ofthe k candidate points, and z-coordinate values of the k candidatepoints; and calculating a median of each of the sorted x-coordinatevalues, the sorted y-coordinate values and the sorted z-coordinatevalues, as the median of the k candidate points.
 7. The method of claim5, wherein, based on the predetermined sampling rate not being 4, acount of a plurality of LoD layers to be used to perform the coding ofthe attributes is calculated based on an equation as follows:${L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},$ where Nis a number of the all points, and k is the predetermined sampling rate.8. An apparatus for adaptive point cloud attribute coding, the apparatuscomprising: at least one memory configured to store computer programcode; and at least one processor configured to access the at least onememory and operate according to the computer program code, the computerprogram code comprising: first determining code configured to cause theat least one processor to determine a centroid of k candidate points ofa point cloud, k being a predetermined sampling rate of all points ofthe point cloud; selecting code configured to cause the at least oneprocessor to select one of the k candidate points that is closest to thecentroid; second determining code configured to cause the at least oneprocessor to determine a first levels-of-detail (LoD) layer comprisingthe one of the k candidate points, and a second LoD layer without theone of the k candidate points; and performing code configured to causethe at least one processor to perform coding of attributes of the allpoints, based on the first LoD layer and the second LoD layer.
 9. Theapparatus of claim 8, wherein, based on the point cloud being a sparsepoint cloud, the predetermined sampling rate is
 3. 10. The apparatus ofclaim 8, wherein, based on the point cloud being a dense point cloud,the predetermined sampling rate is
 4. 11. The apparatus of claim 8,wherein, based on the predetermined sampling rate not being 4, a countof a plurality of LoD layers to be used to perform the coding of theattributes is calculated based on an equation as follows:${L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},$ where Nis a number of the all points, and k is the predetermined sampling rate.12. The apparatus of claim 8, wherein the first determining code isfurther configured to cause the at least one processor to calculate amedian of the k candidate points, the selecting code is furtherconfigured to cause the at least one processor to select another one ofthe k candidate points that is closest to the median, and the seconddetermining code is further configured to cause the at least oneprocessor to determine the first LoD layer comprising the other one ofthe k candidate points, and the second LoD layer without the other oneof the k candidate points.
 13. The apparatus of claim 12, wherein thefirst determining code is further configured to cause the at least oneprocessor to: sort x-coordinate values of the k candidate points,y-coordinate values of the k candidate points, and z-coordinate valuesof the k candidate points; and calculate a median of each of the sortedx-coordinate values, the sorted y-coordinate values and the sortedz-coordinate values, as the median of the k candidate points.
 14. Theapparatus of claim 12, wherein, based on the predetermined sampling ratenot being 4, a count of a plurality of LoD layers to be used to performthe coding of the attributes is calculated based on an equation asfollows: ${L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},$where N is a number of the all points, and k is the predeterminedsampling rate.
 15. A non-transitory computer-readable storage mediumstoring instructions that cause at least one processor to: determine acentroid of k candidate points of a point cloud, k being a predeterminedsampling rate of all points of the point cloud; select one of the kcandidate points that is closest to the centroid; determine a firstlevels-of-detail (LoD) layer comprising the one of the k candidatepoints, and a second LoD layer without the one of the k candidatepoints; and perform coding of attributes of the all points, based on thefirst LoD layer and the second LoD layer.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein, based on thepoint cloud being a sparse point cloud, the predetermined sampling rateis
 3. 17. The non-transitory computer-readable storage medium of claim15, wherein, based on the point cloud being a dense point cloud, thepredetermined sampling rate is
 4. 18. The non-transitorycomputer-readable storage medium of claim 15, wherein, based on thepredetermined sampling rate not being 4, a count of a plurality of LoDlayers to be used to perform the coding of the attributes is calculatedbased on an equation as follows:${L = {{floor}\left( \frac{\log_{10}N}{\log_{10}k} \right)}},$ where Nis a number of the all points, and k is the predetermined sampling rate.19. The non-transitory computer-readable storage medium of claim 15,wherein the instructions further cause the at least one processor to:calculate a median of the k candidate points; select another one of thek candidate points that is closest to the median; and determine thefirst LoD layer comprising the other one of the k candidate points, andthe second LoD layer without the other one of the k candidate points.20. The non-transitory computer-readable storage medium of claim 19,wherein the instructions further cause the at least one processor to:sort x-coordinate values of the k candidate points, y-coordinate valuesof the k candidate points, and z-coordinate values of the k candidatepoints; and calculate a median of each of the sorted x-coordinatevalues, the sorted y-coordinate values and the sorted z-coordinatevalues, as the median of the k candidate points.